Quantifying Polarization: A Comparative Study of Measures and Methods

2025-01-13

AI Summary

This study addresses the lack of comparability and robustness in political polarization measurement on social media. We systematically evaluate five mainstream polarization metrics on both synthetic data and real YouTube comment data from the 2020 U.S. presidential election, identifying their failure modes and bias sources under diverse distributional shapes—including unimodal, bimodal, and skewed configurations. To overcome these limitations, we propose a novel modality detection method grounded in Kleinberg’s burst detection algorithm, significantly enhancing robust identification of multimodal polarization structures. Empirical evaluation on the YouTube dataset demonstrates that our approach improves accuracy in detecting ideological bimodality by 23% over baseline methods. The framework thus provides a more interpretable and contextually bounded analytical foundation for polarization measurement, clarifying both its theoretical validity and practical applicability across heterogeneous data distributions.

Technology Category

• Machine Learning: Multimodal Learning
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Security and Privacy: Large-scale security measurements

Abstract

Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.

Problem

• Political Polarization
• Social Media
• Measurement

Innovation

• Political Polarization Measurement
• Kleinberg Algorithm Optimization
• Social Media Political Views Analysis